In the past it has been difficult for providers of knowledge based services, such as healthcare professionals to increase their productivity in terms of clients (or patients) seen per day. This is because at present, it is necessary for service providers, such as doctors, dentists, physiotherapists, optometrists, podiatrists, to attend to the patient during the entire consultation process. This list is given by way of example only, it is not meant to be exhaustive, nor to indicate a particular class of knowledge based service providers. Nevertheless, the remainder of the present discussion will use the field of healthcare professionals in order to illustrate the concepts involved.
An effective and competent health professional easily builds up a large practice and with constant referrals from within and outside the health care network, may soon be overwhelmed with patient numbers. This limits the time available for solving the problems of the individual patient and hence translates into a deterioration of health care for each of the individual patients once the healthcare professional exceeds his or her limit of patient numbers.
In addition, the potential income of a healthcare professional is presently limited by the time available to spend with each patient. Therefore, despite the arduous acquisition of expert knowledge and skills, the potential income of healthcare professionals is at a relative disadvantage compared with other professions such as in the legal, accounting or architectural professions. In these latter professions, the situation is compounded because the professionals are better able to leverage their skills by employing junior staff to perform many of the less challenging tasks.
This problem is particularly acute for the successful healthcare professional who attracts a large following of patients. Hitherto it has been difficult to increase patient throughput. For example, the average doctor is mentally exhausted by dealing with forty patients a day with common but difficult symptomatology such as chest pains, tiredness, abdominal discomfort, headaches, shortness of breath, swelling in the legs, dizziness, tiredness, loss of weight, weight gain and chronic diarrhea. To analyse each of these problems properly takes precious time and skill.
Overall quality of the consultation and patient care involves an effective evaluation of patient problems in order to lead to an accurately defined diagnoses and treatment goals/management plans. To provide quality healthcare services requires a clear understanding of the clinical status of the patient and activation of best practice management plans for each delineated problem. In addition, it involves the healthcare professional enlisting the patient to be as cognizant as possible of his or her overall problems as well as the patients support for the treatment plans.
The current healthcare professional consultation model (and in particular that utilised by doctors) is flawed by the limited capacity of the human brain to track and evaluate the multitude of complex patient events. Healthcare professionals are naturally inclined to restrict patient numbers to obviate this problem.
Improved productivity may be achieved by addressing some or all of the following key problems:    1. The limited availability of time;    2. Problems associated with the consultation process, including;            (a) The problem of entering patient historical notes into computerized medical packages;        (b) The problem and time consuming task of analysing patient symptoms;        (c) The duty of care owed to the patient;            3. The problem of the patient-computer interface;    4. The problem of augmenting problem solving capacity from a plurality of resources;    5. The need to link various problem solving resources together; and1. The limited availability of time;
The problem is the apparent need to spend time with the patient during the period in which they are seeking healthcare advice. This can be referred to as ‘the synchronous nature of the consultation.’ Appointments are each made for a block of time during which the healthcare professional spends time face to face with the patient. As the healthcare professional can only be in one place at one time, this is an apparently insurmountable lack of time (or ‘time trap’).
From the patient's viewpoint, it is considerably inconvenient that he or she cannot obtain a consultation, attuned to their current health context, at any time or place.
2. Problems associated with the consultation process, including;
(a) The problem of entering patient historical notes into computerized medical packages;
                Previously proposed procedures, require healthcare professionals to type in patient notes, click on a picklist or use voice control input during the consultation. Such procedures are not admitted to be part of the common general knowledge. They have been found to extend the time required for a consultation. In addition, this triangular process (which involves the computer, the patient and the doctor) will materially affect the quality of the medical thinking and decrease the value of the consultation process. For example, it has been found that a doctor's concentration on the computer screen rather than the patient can inhibit free communication and make the doctor appear more concerned about taking correct notes than about the patient.(b) The problem and time consuming task of analysing patient symptoms;        The typical medical consultation comprises the phases of history taking, physical examination, and assessment. This is followed up by investigations or treatment with drugs or procedures. The history component of a medical consultation is the most time consuming process that requires great concentration on the part of the healthcare professional.        Some of the key components which go together to make up good clinical skills include memory, associations between presented data and the facility to make inferences from clear or fuzzy data. The healthcare professional has to make constant evaluations after detecting each symptom or sign. In order to home in on a diagnosis, the healthcare professional must pose the most appropriate question or look for a contributory and incriminating sign that supports his or her hypotheses. Often these symptoms are difficult and tedious to analyse, and may consume large amounts of expensive and valuable time.        When under pressure due to large numbers of patients, healthcare professionals tend to succumb to mental laziness, and employ simplistic thinking. This often leads to the ordering of more diagnostic tests which may be unnecessary or even inappropriate.(c) The duty of care owed to the patient;        With the increasing complexity of medical care it is getting harder for the healthcare professional, especially the primary care physician or general practitioner to take good care of their patients. For example, someone with diabetes mellitus needs regular eye specialist referral, foot care, dietary advice, blood tests for HbA1c, glucose, lipids. Likewise there are specific long term management protocols for many other chronic diseases, such as ischemic heart disease and asthma.        Other necessary tasks for the healthcare professional in this regard include:        i) check to make sure that all abnormal tests are followed up with more tests, treatment or referral        ii) check for disease-drug interactions        iii) check for drug—drug interactions        iv) try to explain any presenting symptoms with iatrogenic actions        v) heck to make sure that all diagnoses are covered with some form of treatment—example a patient with gout should be offered at least allopurinol to reduce the uric acid        vi) patient is up to date with preventive health checks3. The problem of the patient-computer interface;        
In the past, certain methods utilized paper based or computer forms for example, with check lists. In certain instances, they assist in the creation of a plethora of information regarding the patient. However such a large amount of information has been found to confound the doctor's analysis. Therefore, they have been found to add little in the way of solving the patient's problem. In addition, they do not solve the problem of the healthcare professional having to note down the medical history.
4. The problem of augmenting problem solving capacity from a plurality of resources.
One particular problem solving technique includes the step of looking for information on the world wide web. This technique relies on indexing all the words in a web document. This can be problematic since such internet searches typically return tens of thousands of web documents.
Such searches involve machine conversations in the form of SGML/HTML/XML documents being sent from HTTP servers to web browsers and CGI scripts/VBScripts/Javascripts to servers. The seeking and retrieval of such documents is aided by internet smart engines, web crawlers which index the documents for retrieval by keywords or search categories. With an XML solution, a knowledge broker needs to parse the XML syntax and then parse the contents of the XML document. So really the XML solution is just pushing the problem one layer deeper and in no way solves the problems outlined in this background. The poor efficiency of present systems is compounded by the namespace problem in the knowledge domain. This is illustrated by the word ‘driver’ which may relate to a driver of a car or a software driver, depending on the context.
5. The need to link various problem solving resources together;
Present computer—based healthcare systems rely on numeric coding methods such as the International Classification Diseases edition 9 and 10. Basically, such systems use the concept of mapping textual descriptions to a fixed predominantly number code. Numeric based coding schemes have led to an explosion of the terms list. Maintenance is a gargantuan task and inevitably the complexity leads to collapse of the system.
The present systems for medical coding use a call by reference coding system. According to these systems, the disease and illness contexts are lumped together to be given a fixed code. A medical code often describes a central theme and has associated modifiers—for example ‘injury involving cycling and traffic accident’. According to the present systems, a medical code is analogous to a gelati in which the type of cone (theme) and the flavours of gelati (modifiers) are specified. Hence a cone/gelati combination can be ordered by specifying a reference code on the wall or by naming.
Examples of reference coding are the V series injury codes in ICD10 where there are 98 modifiers or gelati flavours to injury. Even though there are only 11,000 slots for V codes, there are 3,764,376 combinations from 3 or 4 modifiers from the pool of 98. Only a theoretical 1% (11,000 out of 3,764,376) of contextual situations have been coded exactly right.
Such call by reference coding systems (such as ICD, ICPC, Read and Snomed) have been found to have a likelihood of an adverse outcome for patients or a component of the health system. Such errors are likely to arise from the loss of contextual information or misinformation, due to the complexity of using or maintaining such a system—this is termed the gelati syndrome.
In a nutshell, the problem that coding in medicine faces can be reduced to the issues of efficiency of data handling and integrity of data. Previously used reference codes such as Read or Snomed which are modeled on the numeric paradigm appear unable to adequately deal with the problems faced by e-medicine.
Such previous codes such as Snomed are rigid multi-axial systems which map a mainly rigid numeric string to a textual description of the target medical entity that may contain a plurality of contextual information.
A further problem with present medical coding systems is their inability to handle fine detail or ‘fine grain information’. Such information can be crucial in medical research and lead to picking the tiny nuances that may lead to a breakthrough in understanding a patient's condition. Present systems, such as the V codes in ICD 10 often lead to a loss of coded information due to loss of context (for example, there are only 3356 V codes with which to cover all types of injuries.)
A further disadvantage of the V codes is that it has a limited range of discriminants or context modifiers (for example, for injuries). There are a total of 98 discriminants or points of differences in the V codes. These 98 discriminants include: ‘accident’ ‘activities’ ‘agricultural’ ‘air’ ‘aircraft’ ‘alighting’ ‘all-terrain’ ‘animal’ ‘animal-drawn’ ‘animal-rider’ ‘antecedent’ ‘balloon’ ‘boarding’ ‘boat’ ‘bus’ ‘canoe’ ‘car’ ‘causing’ ‘collision’ ‘commercial’ ‘construction’ ‘craft’ ‘cycle’ ‘cyclist’ ‘derailment’ ‘driver’ ‘drowning’ ‘eating’ ‘fall’ ‘fishing’ ‘fixed-wing’ ‘glider’ ‘ground’ ‘hang-glider’ ‘heavy’ ‘helicopter’ ‘hit’ ‘income’ ‘industrial’ ‘inflatable’ ‘injured’ ‘injury’ ‘kayak’ ‘leisure’ ‘merchant’ ‘microlight’ ‘mode’ ‘motor’ ‘motor-’ ‘motor-vehicle’ ‘motorcycle’ ‘noncollision’ ‘nonmotor’ ‘nonmotor-vehicle’ ‘nonpowered’ ‘nonpowered-aircraft’ ‘nontraffic’ ‘object’ ‘occupant’ ‘off-road’ ‘outside’ ‘parachutist’ ‘passenger’ ‘pedal’ ‘pedestrian’ ‘person’ ‘pick-truck’ ‘pick-up’ ‘powered’ ‘powered-glider’ ‘premises’ ‘private’ ‘railway’ ‘rider’ ‘rolling’ ‘sailboat’ ‘ship’ ‘spacecraft’ ‘sports’ ‘stationary’ ‘stock’ ‘streetcar’ ‘submersion’ ‘three-wheeled’ ‘thrown’ ‘traffic’ ‘train’ ‘transport’ ‘truck’ ‘unpowered’ ‘van’ ‘vehicle’ ‘victim's’ ‘water’ ‘water-skis’ ‘water-transport-related’ ‘watercraft’ ‘work’.
The V series of codes are based on the template of Vnn.n[n], where there is a capital V with 2 digits before and one or two digits after the decimal point. The number of V codes possible for 3 digits: 10×10×10×10=1,000. The number of V codes possible for 4 digits: 10×10×10×10=10,000. This gives a maximum total of 11,000 possible V codes, without a total rewrite of the V codes series which would lose the multi-axial information encoded in the number and its location within the code.
The V series usually have three or four descriptor sub-themes. Using the nCr formula:
With the 98 discriminants, if we choose 3 out of 98 we get: 152,096 permutations
With the 98 discriminants, if we choose 4 out of 98 we get: 3,612,280 permutations
Based on the assumption that 98 discriminants are all the health and related profession and medical research community will ever need, the number of code space needed is at least:3,612,280+152,096=3,764,376
From this it can be seen that there is a gross mismatch between the contextual situations that the user wants to code and the codes allowed for by the V series design. There are 3,764,376−11,000=3,753,376 contextual combinations that may be required but which have not been expressed in the V code series. In short only a theoretical 1% (11,000 out of 3,764,376) of contextual situations are coded exactly right. In other words, 99% of contextual situations are coded wrong, do not have a code assigned to them or are lumped into the unspecified activity categories.
This has resulted in some interesting ICD codes with dubious contexts: “V81.70”, “Occupant of railway train or railway vehicle injured in derailment without antecedent collision, while engaged in sports activity”, “Y”,
“V81.71”, “Occupant of railway train or railway vehicle injured in derailment without antecedent collision, while engaged in leisure activity”, “Y”,
A still further problem with the V code system is that due to the mismatch between the number of codes and the combinations possible with the discriminants, coded information must be retrieved via a lookup table and not by inspection of the ICD codes themselves.
For example, the discriminant ‘alighting’ is found in both V codes below, yet there is no location specific digit representation of ‘alighting’ common in both V codes. This is to be expected as the coding space only allows 1 percent of possibilities. In other words, the user will have to look up the ICD table to see what was actually being coded.
“V10.30”, “Pedal cyclist injured in collision with pedestrian or animal, while boarding or alighting, while engaged in sports activity”, “Y”,
“V43.4”, “Car occupant injured in collision with car, pick-up truck or van, while boarding or alighting”, “N”,
A further problem with the numeric reference code schema is the imprecision and difficulty of coding at the source. For example, what happens to the case of the professional golfer hurt with a golf buggy while engaged in coaching a Japanese tourist at Cape Schanck? It needs to be coded as V99.90, V99.91, V99.91, V99.92 or is it V98.8 or V98.94 or V98.99 or just V99 or all of the above. Would the system allow a couple of V codes? Would not a single unified code do? This makes coding very prone to error. It makes training and staffing of coders a very specialised and arduous task—like the teaching of Latin.
There are also issues as to the proper maintenance of numerical based codes. If the number of discriminants stay the same, then there is no maintenance problem at all. The more the number of discriminants are added the more flaky the system becomes as the location specific number becomes meaningless. However with time the pressure will mount for modifiers such as ‘Vietnam’, ‘Timor’, and even ethnic labels to do useful public health research.
Certain public health officials would want to throw in a large number of sporting events such as the ones at the Sydney 2000 Olympics games. A sports medicine physiotherapist or doctor might be interested in looking at injuries involved in distance running. The V codes are confined to 2×2 decimal digits only—to add new discriminants will throw everything out of kilter. To make the new framework backward compatible is a nightmare. There are over 100 Olympic events, the possible number of valid combinations of injuries is a gargantuan figure. To code for all various possible types and causes of sporting injuries using the ICD paradigm would be an enormous task.
A good coding system must be able to cope with the technical problem of the explosion of terms in medical coding or any area of knowledge coding when necessary (such as when the end user wants coding for tiny nuances), without adding thousands and thousands of number codes. The explosion of codes that was seen in the READ coding system, which reached 400,000—would have maintenance a very difficult task.
A still further issue in respect of numerical based systems is the question of information retrieval. An ICD code once applied creates a situation whereby it is hard to retrieve, or analyse a particular subcomponent of a code. For example the code for pedestrian injuries can be anything from V01 to V09. The digits of the decimal code do not appear to have a numeric pattern that is mapped to a particular activity such as “collision” , a “truck”, a “car” or “sports activity” or “work”.
Since the ICD code (and other similar numeric codes) itself does not have a fixed alphanumeric pattern mapped to an external reality, searching for subcomponents is possible but excruciatingly difficult. To search for all truck injuries, a belief system has to be constructed from the ICD list and all truck injuries are mapped to a collection of ICD V codes that have truck as a possible cause of the injury.
The lack of ‘regular patterns’ means that the moment a single new ICD code is issued, the analytical tools for ICD diseases are deprecated. To update the analytical tool to the level of correctness, each new ICD code has to be analysed, its impact on every subroutine of the analytical machine needs to be assessed and each subroutine has to be individually updated.
Traditional, numerical based systems are limited as to the level of complexity of events described. As time goes by, scientists become more and more curious. They may want to set up a study to see if the gender of the driver plays a role in serious car accidents. The colour of the vehicle may play a role in serious car accidents and may come up for serious contemplation. The relative damaging results of side on, versus head on collisions may be a topic for a master's dissertation.
From another angle, some minor social practices may lead to profound changes in a disease demographics. The tiny nuances are the source of many medical breakthroughs. An example is the use of condoms or the practice of circumcision in the spread of AIDS. A disease representational system must be able to code for these tiny variations as a minimum requirement. In that sense the current ICD scheme is not sensitive or expressive enough to code for a comprehensive picture that is required by the researchers. These same researchers might create an ad hoc series of codes to be used once and discarded.
Associated with the problem of coding for medical entities (or any entity in any knowledge domain) there is the problem of converting handbooks and textbooks guidelines into fully fledge computerized decision support, offering answers at the point of clinical decision making. This is distinct from conversion of text material into a series of lookup pages in HTML housed in a web browser. The type of decision support required is more similar to the situation where the user is doing data entry into a clinical recording system and by clicking on a button, the computer will respond with the most pertinent treatment plans tailored to that particular patient.
An example of the type of rule to be captured is:
If the patient with endocarditis is an adult, and has no allergy to penicillin, then give:                1) Benzylpenicillin 2 g 4 hourly intravenously for 2 weeks        OR        2) Gentamycin 2 mg/kg 4 hourly intravenously for 7 days, monitoring blood levels.        OR        3) Vancomycin 1 g intravenously 12 hourly PLUS Gentamycin 2 mg/kg 4 hourly intravenously, both for 3 days.        
Coding the above with a numeric coding system is a challenge.